The present invention generally relates to the field of cardiology and in particular to detection and analysis of cardiac function. There is a serious need for detection of normal and abnormal cardiac rhythms as well as evaluation of the clinical status of the patient, e.g., detection of severe or worsening congestive heart failure, using heart rate (HR) or interbeat interval series.
A problem of enormous and growing concern in health care in America is hospitalization for worsening congestive heart failure (CHF). New medical therapies have prolonged the life of many with CHF, and implantable cardiac devices—implantable cardioverter-defibrillators (ICDs) and biventricular pacemakers—have been especially effective in prolonging life and reducing symptoms. ICDs are small battery-powered electrical impulse generators that are implanted in at-risk patients and are programmed to detect cardiac arrhythmia and correct it by delivering a jolt of electricity to the heart muscle. Most patients with single lead ICDs have reduced Left Ventricle (LV) function, and thus either have or are at risk for CHF syndromes. Other than heart rate and heart rate variability, and in some cases trans-thoracic impedance, no measures are currently available to gauge the degree of CHF over time. There is, however, potentially a great deal of clinical utility in doing so.
A new role for ICDs is as diagnostic monitors that might allow early detection of incipient volume overload. For example, modern pacemakers and defibrillators store several dimensions of physiological data representative of the functional status or physiological signals of the patient, including:                heart rate (HR)        heart rate variability (HRV)        amount of pacing in the atrium and the ventricle        patient activity, in hours per day        atrial fibrillation burden (only in devices with atrial leads)        arrhythmia log        respiration        trans-thoracic impedance, a measure of pulmonary vascular congestion        and any other relevant physiological signals        
The hope is that all of these parameters will yield clinically useful information about the status of the cardiovascular system and in particular the possibility of imminent decompensation. The presumption is that very early detection of volume overload can be treated at home with increased doses of medications, averting severe symptoms and the need for hospitalization.
These parameters, however, are currently presented to the physician for review without presenting any interpretation, and there are few studies of how these data can be of clinical use. It has been demonstrated that hospitalizations for heart disease is associated with a reduction in heart rate variability (HRV, a well-established measure of risk of cardiac events) measured by the standard deviation of 5-minute median A-A intervals (SDAAM) (the time between sensed, that is, non-paced, atrial depolarizations), reduction in patient activity, and increased heart rate (HR) at night. Although a patient with CHF may exhibit low HRV, there are usually a few beats that are distinct from the rest and will occur prematurely, followed by an extended pause so that the heart can catch-up to where it should have been absent the premature beat. These are termed premature ventricular beats or contractions (PVCs).
Although atrial fibrillation (AF) can be discerned using coefficient of sample entropy (COSEn), attempts at developing a diagnostic tool that distinguishes normal sinus rhythm (NSR) in patients with CHF from other patients with NSR using only very short heart rate time series have so far not succeeded. Such a method would be very useful in patients with ICDs, where the risk of CHF is high but the ability to do extended calculations is low. The long-felt need for a new method that addresses the limitations, disadvantages, and problems discussed above is evidenced by the many databases available for development and testing of new arrhythmia detection algorithms. Several of these databases, such as the MIT-BIH database, have been used during the development and testing of embodiments of the present disclosure.
Detection of AF can be accomplished with very high degrees of accuracy if an intra-atrial cardiac electrogram from an implanted pacing lead or a conventional EKG signal from skin electrodes is available. Neither is as non-obtrusive as a device that records the time from one arterial pulse waveform to the next, but such a non-invasive device can provide only the heart rate time series with no information about cardiac electrical activity. Thus, an algorithm and computer method for detecting arrhythmia or the clinical status of a patient using only a heart rate or pulse rate series is a desirable goal.
There is currently exists no single parameter to inform clinicians and patients of imminent problems such as CHF. Yet such an approach is sensible—combinations of data values may define specific profiles of clinical status. For example, a measure that combines an HRV measure, patient activity and nocturnal heart rate is very likely to be more useful than any of the measures alone. An aspect of an embodiment of the present invention comprises among other things, combining multiple data streams using optimized mathematical techniques.